Vanishing point determination, symmetry-based boundary refinement, and component detection for vehicle object detection or other applications

ABSTRACT

A method includes obtaining, using at least one processing device, a refined boundary identifying a specified portion of a detected object within a scene, where the refined boundary is associated with image data. The method also includes repeatedly, during multiple iterations and using the at least one processing device, (i) identifying multiple regions within the refined boundary and (ii) determining a similarity of the image data contained within the multiple regions. In addition, the method includes identifying, using the at least one processing device, one or more locations of one or more components of the detected object based on the identified regions and the determined similarities.

CROSS-REFERENCE TO RELATED APPLICATION AND PRIORITY CLAIM

This application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application No. 63/249,732 filed on Sep. 29, 2021.This provisional application is hereby incorporated by reference in itsentirety.

TECHNICAL FIELD

This disclosure relates generally to object detection systems. Morespecifically, this disclosure relates to vanishing point determination,symmetry-based boundary refinement, and component detection for vehicleobject detection or other applications.

BACKGROUND

Identifying nearby, moving, or other objects in a scene is often animportant or useful function in many autonomous applications, such as invehicles supporting advanced driving assist system (ADAS) or autonomousdriving (AD) features, or other applications. Performing accurate objectdetection often involves the use of complex computer vision algorithmsor complex deep neural networks. Unfortunately, these algorithms tend torequire larger amounts of computing resources and can generate resultsmore slowly than desired, particularly for automotive applications.

SUMMARY

This disclosure provides vanishing point determination, symmetry-basedboundary refinement, and component detection for vehicle objectdetection or other applications.

In a first embodiment, a method includes obtaining, using at least oneprocessing device, image data associated with a scene. The method alsoincludes identifying, using the at least one processing device, multipleline segments based on the image data. The method further includesidentifying, using the at least one processing device, one or moreboundaries around one or more objects detected in the image data. Inaddition, the method includes estimating, using the at least oneprocessing device, a position of a vanishing point associated with theimage data based on multiple collections of the line segments whileexcluding, from the multiple collections, one or more of the linesegments that overlap with or that are included within the one or moreboundaries.

In a second embodiment, an apparatus includes at least one processingdevice configured to obtain image data associated with a scene. The atleast one processing device is also configured to identify multiple linesegments based on the image data. The at least one processing device isfurther configured to identify one or more boundaries around one or moreobjects detected in the image data. In addition, the at least oneprocessing device is configured to estimate a position of a vanishingpoint associated with the image data based on multiple collections ofthe line segments while excluding, from the multiple collections, one ormore of the line segments that overlap with or that are included withinthe one or more boundaries.

In a third embodiment, a non-transitory machine-readable medium containsinstructions that when executed cause at least one processor to obtainimage data associated with a scene. The medium also containsinstructions that when executed cause the at least one processor toidentify multiple line segments based on the image data. The mediumfurther contains instructions that when executed cause the at least oneprocessor to identify one or more boundaries around one or more objectsdetected in the image data. In addition, the medium containsinstructions that when executed cause the at least one processor toestimate a position of a vanishing point associated with the image databased on multiple collections of the line segments while excluding, fromthe multiple collections, one or more of the line segments that overlapwith or that are included within the one or more boundaries.

In a fourth embodiment, a method includes obtaining, using at least oneprocessing device, a vanishing point and a boundary based on image dataassociated with a scene, where the boundary is associated with adetected object within the scene. The method also includes repeatedly,during multiple iterations and using the at least one processing device,(i) identifying multiple patches within the boundary and (ii)determining a similarity of the image data contained within the multiplepatches. The method further includes identifying, using the at least oneprocessing device, a modification to be applied to the boundary based onthe identified patches and the determined similarities. In addition, themethod includes generating, using the at least one processing device, arefined boundary based on the modification, where the refined boundaryidentifies a specified portion of the detected object.

In a fifth embodiment, an apparatus includes at least one processingdevice configured to obtain a vanishing point and a boundary based onimage data associated with a scene, where the boundary is associatedwith a detected object within the scene. The at least one processingdevice is also configured to repeatedly, during multiple iterations, (i)identify multiple patches within the boundary and (ii) determine asimilarity of the image data contained within the multiple patches. Theat least one processing device is further configured to identify amodification to be applied to the boundary based on the identifiedpatches and the determined similarities. In addition, the at least oneprocessing device is configured to generate a refined boundary based onthe modification, where the refined boundary identifies a specifiedportion of the detected object.

In a sixth embodiment, a non-transitory machine-readable medium containsinstructions that when executed cause at least one processor to obtain avanishing point and a boundary based on image data associated with ascene, where the boundary is associated with a detected object withinthe scene. The medium also contains instructions that when executedcause the at least one processor to repeatedly, during multipleiterations, (i) identify multiple patches within the boundary and (ii)determine a similarity of the image data contained within the multiplepatches. The medium further contains instructions that when executedcause the at least one processor to identify a modification to beapplied to the boundary based on the identified patches and thedetermined similarities. In addition, the medium contains instructionsthat when executed cause the at least one processor to generate arefined boundary based on the modification, where the refined boundaryidentifies a specified portion of the detected object.

In a seventh embodiment, a method includes obtaining, using at least oneprocessing device, a refined boundary identifying a specified portion ofa detected object within a scene, where the refined boundary isassociated with image data. The method also includes repeatedly, duringmultiple iterations and using the at least one processing device, (i)identifying multiple regions within the refined boundary and (ii)determining a similarity of the image data contained within the multipleregions. In addition, the method includes identifying, using the atleast one processing device, one or more locations of one or morecomponents of the detected object based on the identified regions andthe determined similarities.

In an eighth embodiment, an apparatus includes at least one processingdevice configured to obtain a refined boundary identifying a specifiedportion of a detected object within a scene, where the refined boundaryis associated with image data. The at least one processing device isalso configured to repeatedly, during multiple iterations, (i) identifymultiple regions within the refined boundary and (ii) determine asimilarity of the image data contained within the multiple regions. Theat least one processing device is further configured to identify one ormore locations of one or more components of the detected object based onthe identified regions and the determined similarities.

In a ninth embodiment, a non-transitory machine-readable medium containsinstructions that when executed cause at least one processor to obtain arefined boundary identifying a specified portion of a detected objectwithin a scene, where the refined boundary is associated with imagedata. The medium also contains instructions that when executed cause theat least one processor to repeatedly, during multiple iterations, (i)identify multiple regions within the refined boundary and (ii) determinea similarity of the image data contained within the multiple regions.The medium further contains instructions that when executed cause the atleast one processor to identify one or more locations of one or morecomponents of the detected object based on the identified regions andthe determined similarities.

Other technical features may be readily apparent to one skilled in theart from the following figures, descriptions, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure and its advantages,reference is now made to the following description taken in conjunctionwith the accompanying drawings, in which like reference numeralsrepresent like parts:

FIG. 1 illustrates an example system supporting vanishing pointdetermination according to this disclosure;

FIG. 2 illustrates an example method for vanishing point determinationaccording to this disclosure;

FIG. 3 illustrates an example vanishing point determination according tothis disclosure;

FIG. 4 illustrates an example system supporting symmetry-based boundaryrefinement according to this disclosure;

FIG. 5 illustrates an example method for symmetry-based boundaryrefinement according to this disclosure;

FIG. 6 illustrates an example symmetry-based boundary refinementaccording to this disclosure;

FIG. 7 illustrates an example system supporting component detectionaccording to this disclosure;

FIG. 8 illustrates an example method for component detection accordingto this disclosure;

FIG. 9 illustrates an example component detection according to thisdisclosure;

FIG. 10 illustrates an example usage of vanishing point determination,symmetry-based boundary refinement, and/or component detection accordingto this disclosure;

FIG. 11 illustrates an example design flow for employing one or moretools to design hardware that implements one or more control functionsaccording to this disclosure; and

FIG. 12 illustrates an example device supporting execution of one ormore tools to design hardware that implements one or more controlfunctions according to this disclosure.

DETAILED DESCRIPTION

FIGS. 1 through 12 , described below, and the various embodiments usedto describe the principles of this disclosure are by way of illustrationonly and should not be construed in any way to limit the scope of thisdisclosure. Those skilled in the art will understand that the principlesof this disclosure may be implemented in any type of suitably arrangeddevice or system.

As noted above, identifying nearby, moving, or other objects in a sceneis often an important or useful function in many autonomousapplications, such as in vehicles supporting advanced driving assistsystem (ADAS) or autonomous driving (AD) features, or otherapplications. Performing accurate object detection often involves theuse of complex computer vision algorithms or complex deep neuralnetworks. Unfortunately, these algorithms tend to require larger amountsof computing resources and can generate results more slowly thandesired, particularly for automotive applications.

In one aspect, this disclosure provides techniques for determiningvanishing points in images of scenes associated with a vehicle (oftenreferred to as a target vehicle). As described in more detail below, animage of a scene associated with a target vehicle (such as an image ofthe scene in front of the target vehicle) can be captured and processedto identify the vanishing point in the image. In an image containing aset of parallel or substantially parallel lines in three-dimensionalspace (such as lane-marking lines), the lines would form a generallytriangular shape in the image plane due to the perspective of a devicecapturing the image. A vanishing point is defined as the point at whichthe lines in the set converge within the image plane. The determinationof the vanishing point in an image is useful in various image processingapplications, such as in algorithms used for line or lane estimation,indirect measurements of vehicle pitching angles, and identification ofego/non-ego lane driving vehicles.

In another aspect, this disclosure provides techniques for performingsymmetry-based boundary refinement in order to refine one or moreboundaries for one or more objects detected in a scene around a targetvehicle. As described in more detail below, once a bounding box or otherboundary roughly identifying another vehicle is determined in an imageof a scene around a target vehicle, a symmetry-based approach can beused to locate a more-accurate boundary around a portion of the othervehicle (such as around a rear of the other vehicle) based on anexpected symmetry of the other vehicle. For example, another vehicledriving ahead of or towards the target vehicle is expected to have agenerally-symmetrical shape, which can be used to identify amore-accurate boundary around a portion of the other vehicle in theimage. In some cases, to avoid reliance on the other vehicle's specificmake/type/model, images may be subjected to no or minimal enhancements(such as only. Sobel edge detection or other edge detection) without anypre-trained patch/template matching. The determination of more-accuratevehicle boundaries can be useful in various applications, such asidentifying a direction of travel or a change in the direction of travelof the other vehicle relative to the target vehicle or identifying asurface of the other vehicle to be used for depth estimation.

In still another aspect, this disclosure provides techniques forperforming component detection in order to identify one or more specificcomponents on the rear portion(s) or other portion(s) of at least oneother vehicle in a scene around a target vehicle. As described in moredetail below, once the boundary of another vehicle is determined andrefined, the image data associated with the other vehicle within therefined boundary can be analyzed to identify one or more regions of theother vehicle associated with one or more specific components of theother vehicle, such as rear taillights or a license plate of the othervehicle. Again, this can be accomplished without prior knowledge ofother vehicle's specific make/type/model. This type of information canbe useful in various applications, such as identifying a direction oftravel or a change in the direction of travel of the other vehiclerelative to the target vehicle or identifying a surface of the othervehicle to be used for depth estimation.

In this way, these techniques can be used to identify variousinformation (such as a vanishing point, a symmetry-based boundary,and/or detected vehicle components) that is useful in performing one ormore functions, such as one or more ADAS or AD functions. Moreover, thisinformation can be determined using less computing resources and infaster times, which enables use of this information inresource-constrained applications and in real-time applications.

FIG. 1 illustrates an example system 100 supporting vanishing pointdetermination according to this disclosure. In this particular example,the system 100 takes the form of an automotive vehicle, such as anelectric vehicle. However, any other suitable system may supportvanishing point determination, such as other types of vehicles,autonomous robots, or other autonomous or non-autonomous systems.

As shown in FIG. 1 , the system 100 includes at least one processor 102configured to control one or more operations of the system 100. In thisexample, the processor 102 may interact with one or more sensors 104 andwith one or more components coupled to a bus 106. In this particularexample, the one or more sensors 104 include one or more cameras 104 aor other imaging sensors, and the bus 106 represents a controller areanetwork (CAN) bus. However, the processor 102 may interact with anyadditional sensor(s) and communicate over any other or additionalbus(es).

The one or more cameras 104 a are configured to generate images ofscenes around the system 100. Note that other or additional types ofsensors may be used here, such as one or more radio detection andranging (RADAR) sensors, light detection and ranging (LIDAR) sensors,other types of imaging sensors, or inertial measurement units (IMUs).Measurements or other data from the sensors 104 are used by theprocessor 102 or other component(s) as described below to performvarious functions. In some cases, the sensors 104 may include a singlecamera 104 a, such as one camera positioned on the front of a vehicle.In other cases, the sensors 104 may include multiple cameras 104 a, suchas one camera positioned on the front of a vehicle, one camerapositioned on the rear of the vehicle, and two cameras positioned onopposite sides of the vehicle.

The processor 102 can process the information from the sensors 104 inorder to detect objects around or proximate to the system 100, such asone or more vehicles, obstacles, or people near the system 100. Theprocessor 102 can also process the information from the sensors 104 inorder to perceive lane-marking lines or other markings on a road, floor,or other surface. The processor 102 can further use various informationto generate predictions associated with the system 100, such as topredict the future path(s) of the system 100 or other vehicles, identifya center of a lane in which the system 100 is traveling, or predict thefuture locations of objects around the system 100.

In this example, the processor 102 performs an object detection function108, which generally operates to identify objects around the system 100in a real-time manner based on images or other measurements from thesensor(s) 104. For example, the object detection function 108 can useimages from one or more cameras 104 a or data from other sensors 104 toidentify external objects around the system 100, such as other vehiclesmoving around or towards the system 100 or pedestrians or objects nearthe system 100. The object detection function 108 can also identify oneor more characteristics of each of one or more detected objects, such asan object class (a type of object) and a boundary around the detectedobject. The processor 102 may use any suitable technique to identify theobjects around the system 100 based on data from the sensor(s) 104.Various techniques for object detection are known in the art, andadditional techniques for object detection are sure to be developed inthe future. Any of these techniques may be used by the processor 102here to implement the object detection function 108.

The processor 102 also performs a line segment detection function 110,which generally operates to identify line segments within the imagesfrom one or more cameras 104 a or the data from other sensors 104. Eachdetected line segment may generally represent a portion in an image orother sensor data identifying a relatively straight feature. Asparticular examples, detected line segments may represent portions oflane-marking lines or other markings on a road, floor, or other surface,edges of nearby buildings or other nearby structures; or edges or otherfeatures of other objects captured in images or other data. Theprocessor 102 may use any suitable technique to identify line segmentsbased on data from the sensor(s) 104. Various techniques for linesegment detection are known in the art, and additional techniques forline segment detection are sure to be developed in the future. Any ofthese techniques may be used by the processor 102 here to implement theline segment detection function 110.

The processor 102 further performs an incremental aggregation function112 and a vanishing point detection function 114. The incrementalaggregation function 112 generally operates to identify differentcollections of the line segments detected by the line segment detectionfunction 110 and to estimate a crossing point of the line segments foreach collection (which may also involve determination of a residualvalue). The vanishing point detection function 114 generally operates tocalculate a vanishing point for the image based on the determinedcrossing points, such as by using the residual values as weights duringa weighted combination of the crossing points. Example operations thatcan be performed by the incremental aggregation function 112 and thevanishing point detection function 114 are described below. The finalvanishing point determined for a scene may be used in any suitablemanner, including in the techniques described below.

Note that the functions 108-114 shown in FIG. 1 and described above maybe implemented in any suitable manner in the system 100. For example, insome embodiments, various functions 108-114 may be implemented orsupported using one or more software applications or other softwareinstructions that are executed by at least one processor 102. In otherembodiments, at least some of the functions 108-114 can be implementedor supported using dedicated hardware components. In general, thefunctions 108-114 described above may be performed using any suitablehardware or any suitable combination of hardware and software/firmwareinstructions.

The processor 102 itself may also be implemented in any suitable manner,and the system 100 may include any suitable number(s) and type(s) ofprocessors or other processing devices in any suitable arrangement.Example types of processors 102 that may be used here include one ormore microprocessors, microcontrollers, digital signal processors(DSPs), application specific integrated circuits (ASICs), fieldprogrammable gate arrays (FPGAs), or discrete circuitry. Each processor102 may also have any suitable number of processing cores or engines. Insome cases, multiple processors 102 or multiple processing cores orengines in one or more processors 102 may be used to perform thefunctions 108-114 described above. This may allow, for instance, theprocessor(s) 102 to be used to process multiple images and other sensordata in parallel or to perform various operations described in thispatent document above and below in parallel.

Although FIG. 1 illustrates one example of a system 100 supportingvanishing point determination, various changes may be made to FIG. 1 .For example, various functions and components shown in FIG. 1 may becombined, further subdivided, replicated, omitted, or rearranged andadditional functions and components may be added according to particularneeds. Also, as noted above, the functionality for vanishing pointdetermination may be used in any other suitable system, and the systemmay or may not relate to automotive vehicles or other vehicles.

FIG. 2 illustrates an example method 200 for vanishing pointdetermination according to this disclosure. The method 200 may, forexample, be performed using the components of the system 100 shown inFIG. 1 . Note, however, that the method 200 may be performed using anyother suitable device or system. Also, during the discussion of themethod 200, reference is made to FIG. 3 , which illustrates an examplevanishing point determination according to this disclosure. The exampleof the determination shown in FIG. 3 is for illustration only and ismerely meant to illustrate how various steps in FIG. 2 may be performed.

As shown in FIG. 2 , an image of a scene around a vehicle is obtained atstep 202. This may include, for example, the processor 102 obtaining atleast one image from at least one camera 104 a or obtaining other sensordata from at least one other type of sensor 104. One or more objects inthe scene are identified using the image at step 204. This may include,for example, the processor 102 performing the object detection function108 to identify at least one object and a bounding box or other boundaryaround each object. Line segments in the scene are identified using theimage at step 206. This may include, for example, the processor 102performing the line segment detection function 110 to identify segmentsof lane-marking lines, other manmade lines, edges of buildings or otherstructures, or other line segments in the image.

An example of this is shown in FIG. 3 , where an image 300 is processedto identify two bounding boxes 302, 304 associated with two objects andto identify three line segments 306, 308, 310. Bounding boxes may bereferred to below using the notation θ_(bbox,k)=(x_(k), y_(k), w_(k),h_(k)), where k=1, . . . , N. Here, N refers to the total number ofbounding boxes, (x_(α), y_(α)) refers to the coordinates of the pixel atthe top left corner of the k^(th) bounding box, and (w_(k), h_(k))refers to the width and height of the k^(th) bounding box in pixels.Also, line segments may be referred to below using the notation

, where

=1, . . . , M. Here, M refers to the total number of line segments, andeach line segment may be parameterized, such as by using the form

=ax+by +c or by using the form

=s {right arrow over (p)}_(A)+(1−s){right arrow over (p)}_(B),S∈[0,1].

An iterative process occurs to identify the crossing points fordifferent combinations of the identified line segments. A referencehorizon in the image is identified at step 208. This may include, forexample, the processor 102 performing the incremental aggregationfunction 112 to identify a generally horizontal line around the middleof the captured image where it appears the horizon is located. Anexample of this is shown in FIG. 3 , where a line 312 represents anidentified reference horizon.

A set of line segments under the reference horizon is selected at step210. This may include, for example, the processor 102 performing theincremental aggregation function 112 to identify a set of the linesegments that are present under the identified reference horizon in theimage. In some embodiments, the processor 102 can select a set of L linesegments that satisfy the following:

S _(l) =

{

|{right arrow over (p)} _(A)(y)<h _(ref) and {right arrow over (p)}_(B)(y)<h _(ref) },|S _(l) |=L  (1)

Here, h_(ref) refers to the identified reference horizon, and {rightarrow over (p)}_(A)(y) and {right arrow over (p)}_(B)(y) refer to they-coordinates of the ends of the associated line segment. Also, in someembodiments, one or more bounding boxes can be used to filter or ignoreline segments that overlap or that are included within the boundingbox(es), which can be done to help prevent noisy estimations. This canbe expressed as follows:

S _(l)={

|√{square root over ({right arrow over (p)} _(A)∈{right arrow over(θ)}_(bbox,q))} and √{square root over ({right arrow over (p)}b∈θ_(bbox,q))}∀q=1, . . . ,N},|S _(l) |=L  (2)

Here, {right arrow over (p)}∈θ_(bbox) represents a predefined operatorto check if a point {right arrow over (p)} is contained in a boundingbox {right arrow over (θ)}_(bbox).

A crossing point of the line segments in the select set is estimated atstep 212. This may include, for example, the processor 102 performingthe incremental aggregation function 112 to identify a crossing pointfor the L line segments in the set using a “least squares” calculation.In some embodiments, the crossing point may be identified in thefollowing manner:

$\begin{matrix}{{a\hat{X}} = {( {A^{T}A} )^{- 1}A^{T}B}} & (3)\end{matrix}$ where: $\begin{matrix}{{A = \begin{bmatrix}a_{1} & b_{1} & c_{1} \\ \vdots & \vdots & \vdots \\a_{L} & b_{L} & c_{L}\end{bmatrix}},{X = \begin{bmatrix}x & y & 1\end{bmatrix}^{T}},{B = \begin{bmatrix}0 & \ldots & 0\end{bmatrix}^{T}}} & (4)\end{matrix}$

Here, a₁-c₁ represent a vector of values associated with the first linesegment, and a_(L)-c_(L) represent a vector of values associated withthe L^(th) line segment. The least squares calculation or othercalculation may identify a crossing point {right arrow over(p)}_(cross)=({circumflex over (x)}, ŷ) and a residual r. The crossingpoint and its associated residual can be stored at step 214. In somecases, the crossing point and its associated residual can be stored as apair S_(p)←{{right arrow over (p)}_(cross), r} in a cache or othermemory of the processor 102 or other suitable storage.

A determination is made whether to repeat another iteration at step 216.This may include, for example, the processor 102 performing theincremental aggregation function 112 to determine whether auser-specified number or other number of estimated crossing points havebeen identified. If not, the process can return to step 208. Otherwise,a vanishing point is determined using the stored crossing points andresiduals at step 218. This may include, for example, the processor 102performing the vanishing point detection function 114 to calculate thevanishing point for the image using the stored crossing points andresiduals. In some embodiments, the vanishing point detection function114 determines the vanishing point using a weighted combination of thecrossing points, where the residuals are used as the weights for thecombination. This can be expressed as:

$\begin{matrix}{{{\overset{arrow}{p}}_{vanish} = {\sum_{{i = 1},\ldots,K}{{\overset{\sim}{r}}_{i} \cdot {\overset{arrow}{p}}_{cross}}}},{{\overset{\sim}{r}}_{i} = \frac{r_{i}}{\sum_{{j = 1},\ldots,K}r_{j}}}} & (5)\end{matrix}$

Here, K represents the number of crossing points and residuals generatedduring K iterations of steps 208-214. As shown in FIG. 3 , a vanishingpoint 314 may be identified for the image 300. The identified vanishingpoint 314 may be used in various ways, including in the techniquesdescribed below.

In this way, vanishing point estimation may occur via a progressiveaggregation of line segments. This approach can be easily implementedusing less-heavy computing resources and/or can be performed in lesstime. Many of the line segments may be identified using paintedlane-marking lines and other painted lines, while other environmentalline segments (such as from buildings, road curbs, or urban poles) canalso be used. To prevent inconsistent vanishing point estimation, linesegments associated with other vehicles may optionally be filtered, suchas by ignoring line segments within bounding boxes. Among other things,this approach can avoid using heavy computing resources (which arerequired by conventional deep neural networks). In some cases, multipleprocessors 102 and/or multiple cores or engines of the processor(s) 102can be used to perform different portions of the process describedabove.

Although FIG. 2 illustrates one example of a method 200 for vanishingpoint determination, various changes may be made to FIG. 2 . Forexample, while shown as a series of steps, various steps in FIG. 2 mayoverlap, occur in parallel, occur in a different order, or occur anynumber of times. Although FIG. 3 illustrates one example of a vanishingpoint determination, various changes may be made to FIG. 3 . Forinstance, the contents of the image 300 and the results of the vanishingpoint determination can vary depending on the actual environment aroundthe system 100.

The vanishing point determined for an image as described above may beused in any suitable manner. For example, in some cases, the vanishingpoint determined for an image may be used to perform symmetry-basedboundary refinement as described below. Note, however, that this use ofthe vanishing point is for illustration only.

FIG. 4 illustrates an example system 400 supporting symmetry-basedboundary refinement according to this disclosure. The system 400 hereincludes many of the same components described above with respect to thesystem 100, and common reference numbers are used in the systems. Asshown in FIG. 4 , the processor 102 of FIG. 4 performs the objectdetection function 108 and the vanishing point detection function 114.Note that the line segment detection function 110 and the incrementalaggregation function 112 are omitted from FIG. 4 for clarity but can beperformed by the processor 102 of the system 400 in FIG. 4 .

The processor 102 in FIG. 4 is configured to perform symmetry-basedboundary refinement, which generally involves refining one or morebounding boxes or other boundaries for one or more detected objects tomore accurately define the boundary of a specified portion of eachdetected object (such as a front or rear portion of each detectedobject). Each original bounding box from the object detection function108 may be defined using the notation {right arrow over(θ)}_(bbox,k)=(x_(k), y_(k), w_(k), h_(k)) as described above. Eachrefined bounding box that is generated as described below may be definedusing the notation θ_(bbox,k)=(x_(k), y_(k), w_(k), h_(k), π_(l,k),π_(r,k)), where (π_(l,k), π_(r,k))∈[0,1] are ratio parameters thatrepresent the refined bounding box (such as when x_(k)+w_(k)·π_(l,k)represents the refined box's left boundary edge). Thus, each refinedbounding box has a set of (π_(l,i), π_(r,i)) ratios that alter the sizeof the original bounding box.

In this example, the processor 102 performs a multi-template matchingfunction 402 and a bounding box refinement function 404. Themulti-template matching function 402 generally operates to analyze eachbounding box generated by the object detection function 108 using (amongother things) the vanishing point identified by the vanishing pointdetection function 114. The multi-template matching function 402 uses anexpected symmetry of each detected vehicle in an image so that thebounding box refinement function 404 is able to refine the bounding boxassociated with that detected vehicle. As described in more detailbelow, the multi-template matching function 402 can analyze differentpatches on right and left sides of an original bounding box and look forstatistically-significant similarities between the patches on the rightand left sides of the original bounding box. The similarities betweenthe patches can be used to identify a modification to be applied by thebounding box refinement function 404 to update the original bounding boxand generate an updated or refined bounding box. Example operations thatcan be performed by the multi-template matching function 402 and thebounding box refinement function 404 are described below. One or morerefined bounding boxes determined for a scene may be used in anysuitable manner, including in the techniques described below.

Note that the functions 402-404 shown in FIG. 4 and described above maybe implemented in any suitable manner in the system 400. For example, insome embodiments, various functions 402-404 may be implemented orsupported using one or more software applications or other softwareinstructions that are executed by at least one processor 102. In otherembodiments, at least some of the functions 402-404 can be implementedor supported using dedicated hardware components. In general, thefunctions 402-404 described above may be performed using any suitablehardware or any suitable combination of hardware and software/firmwareinstructions.

Although FIG. 4 illustrates one example of a system 400 supportingsymmetry-based boundary refinement, various changes may be made to FIG.4 . For example, various functions and components shown in FIG. 4 may becombined, further subdivided, replicated, omitted, or rearranged andadditional functions and components may be added according to particularneeds. Also, as noted above, the functionality for symmetry-basedboundary refinement may be used in any other suitable system, and thesystem may or may not relate to automotive vehicles or other vehicles.

FIG. 5 illustrates an example method 500 for symmetry-based boundaryrefinement according to this disclosure. The method 500 may, forexample, be performed using the components of the system 400 shown inFIG. 4 . Note, however, that the method 500 may be performed using anyother suitable device or system. Also, during the discussion of themethod 500, reference is made to FIG. 6 , which illustrates an examplesymmetry-based boundary refinement according to this disclosure. Theexample of the refinement shown in FIG. 6 is for illustration only andis merely meant to illustrate how various steps in FIG. 5 may beperformed.

As shown in FIG. 5 , a vanishing point is identified using an image atstep 502. This may include, for example, the processor 102 performingthe process shown in FIG. 2 to identify a vanishing point {right arrowover (p)}_(vanish)=(x,y), which can define the x and y coordinates ofthe determined vanishing point in the image. Left and right virtual egolines are identified in the image at step 504. This may include, forexample, the processor 102 estimating a left virtual ego line {rightarrow over (l)}_(ego,l) as passing through the determined vanishingpoint and through the bottom right corner of a bounding box around anearby vehicle and estimating a right virtual ego line {circumflex over(l)}_(ego,r) as passing through the determined vanishing point andthrough the bottom left corner of a bounding box around another nearbyvehicle. In some cases, the left virtual ego line can be described asfollows:

{right arrow over (l)} _(ego,l) =a _(l) x+b _(l) y+c _(l)  (6)

A similar equation may be used for the right virtual ego line. Anexample of this is shown in FIG. 6 , where the image 300 is processed toidentify a left virtual ego line 602 and a right virtual ego line 604.The left and right virtual ego lines and the bottom of the image areused to define a triangular region within the image at step 506. Anexample of this is shown in FIG. 6 , where the image 300 includes atriangular region 606. The identified triangular region may be denotedP_(triangle).

A bounding box defined for the image is selected at step 508. This mayinclude, for example, the processor 102 performing the multi-templatematching function 402 to select one of the bounding boxes generated bythe object detection function 108. A determination is made whether adefined point of the selected bounding box is inside or outside of thetriangular region at step 510. For convenience, the bottom center point{right arrow over (p)}_(cb) of the selected bounding box may be used asthe defined point, although other points along the selected bounding boxmay be used as the defined point. This may include, for example, theprocessor 102 performing the multi-template matching function 402 todetermine whether the defined point of the selected bounding box isinside the triangular region, to the left of the triangular region, orto the right of the triangular region. In some embodiments, theprocessor 102 may perform the following to make this determination:

$\begin{matrix}{{{\overset{arrow}{p}}_{cb} \subset P_{triangle}} = \{ \begin{matrix}{{{{{if}{{\overset{arrow}{p}}_{cb}(y)}} + {\frac{a_{l}}{b_{l}}{{\overset{arrow}{p}}_{cb}(x)}}} < \frac{c_{l}}{b_{l}}},} & {\,^{\backprime}{left}^{\prime}} \\{{{{{if}{{\overset{arrow}{p}}_{cb}(y)}} + {\frac{a_{r}}{b_{r}}{{\overset{arrow}{p}}_{cb}(x)}}} < \frac{c_{r}}{b_{r}}},} & {\,^{\backprime}{right}^{\prime}} \\{{else},} & {\,^{\backprime}{center}^{\prime}}\end{matrix} } & (7)\end{matrix}$

Here, an object is determined to be within the triangular region(center) or outside the triangular region (left or right). Boundingboxes for objects within the triangular region may be excluded fromfurther processing, while bounding boxes for objects to the left andright of the triangular region may be processed further for refinement.

Assuming the selected bounding box is outside the triangular region, aninitial value for the bounding box ratio is defined at step 512. Thismay include, for example, the processor 102 performing themulti-template matching function 402 to define the initial bounding boxratio. In some cases, initial bounding box ratio values for a boundingbox may be defined as:

$\begin{matrix}{{{\overset{\sim}{\pi}}_{l,i} = {{k_{l}\frac{{{\overset{arrow}{p}}_{cb}(x)} - {{\overset{arrow}{p}}_{{cb},l}^{\prime}(x)}}{{{\overset{arrow}{p}}_{rc}(x)} - {{\overset{arrow}{p}}_{{cb},l}^{\prime}(x)}}} + {( {1 - k_{l}} )\frac{{{\overset{arrow}{p}}_{cb}(y)} - {{\overset{arrow}{p}}_{vanish}(y)}}{{{\overset{arrow}{p}}_{rc}(y)} - {{\overset{arrow}{p}}_{vanish}(y)}}}}},{k_{l} \in \lbrack {0,1} \rbrack}} & (8)\end{matrix}$ $\begin{matrix}{{{\overset{\sim}{\pi}}_{r,i} = {{k_{r}\frac{{{\overset{arrow}{p}}_{{cb},r}^{\prime}(x)} - {{\overset{arrow}{p}}_{cb}(x)}}{{{\overset{arrow}{p}}_{rc}(x)} - {{\overset{arrow}{p}}_{{cb},r}^{\prime}(x)}}} + {( {1 - k_{r}} )\frac{{{\overset{arrow}{p}}_{cb}(y)} - {{\overset{arrow}{p}}_{vanish}(y)}}{{{\overset{arrow}{p}}_{rc}(y)} - {{\overset{arrow}{p}}_{vanish}(y)}}}}},{k_{r} \in \lbrack {0,1} \rbrack}} & (9)\end{matrix}$ where: $\begin{matrix}{{\overset{arrow}{p}}_{{cb},l}^{\prime} = ( {{{- \frac{b_{l}}{a_{l}}{{\overset{arrow}{p}}_{cb}(x)}} + \frac{c_{l}}{a_{l}}},{{\overset{arrow}{p}}_{cb}(y)}} )} & (10)\end{matrix}$ $\begin{matrix}{{\overset{arrow}{p}}_{{cb},r}^{\prime} = ( {{{- \frac{b_{r}}{a_{r}}{{\overset{arrow}{p}}_{cb}(x)}} + \frac{c_{r}}{a_{r}}},{{\overset{arrow}{p}}_{cb}(y)}} )} & (11)\end{matrix}$

An iterative process occurs to identify and analyze patches from theselected bounding box. Left and right patches in the selected boundingbox are selected at step 514. This may include, for example, theprocessor 102 performing the multi-template matching function 402 toselect a patch of the selected bounding box in a left portion of theselected bounding box and to select a patch of the selected bounding boxin a right portion of the selected bounding box. A first of the patcheshere may be selected randomly, in a predefined manner, or in any othersuitable manner, and a second of the patches may be a mirror image ofthe first patch. In some embodiments, the processor 102 may select arectangular-shaped patch {right arrow over (θ)}_(tpl,k)=(x_(k), y_(k),w_(k), h_(k)) from the left edge of the selected bounding box (meaningx_(k)=x_(i)+w_(i)·{tilde over (π)}_(l,i)) and select a mirrored patch{right arrow over (θ)}_(tpl,k) from the residual portion up to the rightedge of the selected bounding box (meaningx_(k)+w_(k)=x_(i)+w_(i)·(1−{tilde over (π)}_(r,i))).

A measure of similarity between the left and right patches is identifiedat step 516. This may include, for example, the processor 102 performingthe multi-template matching function 402 to calculate the visualsimilarity of the image data in the left and right patches. In someembodiments, the similarity can be calculated as a mathematicalcorrelation, such as in the following manner:

S _(k) ,{right arrow over (p)} _(match,k) =f({right arrow over(θ)}_(tpl,k),{right arrow over (θ′)}_(tpl,k))  (12)

Here, s_(k) denotes the visual similarity between the two patches. Thesimilarity and the patch coordinates (which can be said to representtemplate coordinates) can be stored at step 518. This may include, forexample, the processor 102 storing a tuple of {visual similarity s_(k),left template coordinates {right arrow over (p)}_(left-tpl)=g({rightarrow over (p)}_(match,k), {right arrow over (θ)}_(tpl,k)), righttemplate coordinates {right arrow over (p)}_(right-tpl)=h({right arrowover (p)}_(match,k), {right arrow over (θ′)}_(tpl,k))}. This may beexpressed as follows:

S _(s) ←{S _(k) ,{right arrow over (p)} _(left-tpl) ,{right arrow over(p)} _(right-tpl)})  (13)

A determination is made whether to repeat another iteration at step 520.This may include, for example, the processor 102 determining whether auser-specified number or other number of estimated similarities andtemplate coordinates have been identified. If not, the process canreturn to step 514. Otherwise, finalized left and right templatecoordinates are identified at step 522. This may include, for example,the processor 102 performing the multi-template matching function 402 tocalculate the finalized left and right template coordinates using thestored template coordinates and visual similarities. In someembodiments, the multi-template matching function 402 determines thefinalized left and right template coordinates as a weighted combinationof the stored left and right template coordinates, where the visualsimilarities are used as the weights for the combination. This can beexpressed as:

$\begin{matrix}{{{\overset{\_}{\overset{arrow}{p}}}_{{left} - {tpl}} = {\sum_{{k = 1},\ldots,K}{{\overset{\sim}{s}}_{k} \cdot {\overset{arrow}{p}}_{{left} - {tpl}}}}},{{\overset{\sim}{s}}_{k} = \frac{s_{k}}{\sum_{{j = 1},\ldots,K}s_{j}}}} & (14)\end{matrix}$ $\begin{matrix}{{{\overset{\_}{\overset{arrow}{p}}}_{{right} - {tpl}} = {\sum_{{k = 1},\ldots,K}{{\overset{\sim}{s}}_{k} \cdot {\overset{arrow}{p}}_{{right} - {tpl}}}}},{{\overset{\sim}{s}}_{k} = \frac{s_{k}}{\sum_{{j = 1},\ldots,K}s_{j}}}} & (15)\end{matrix}$

The boundary ratio for the selected bounding box is updated and used torefine the selected bounding box at step 524. This may include, forexample, the processor 102 performing the multi-template matchingfunction 402 to update the boundary ratio. In some embodiments, this canbe expressed as follows:

$\begin{matrix}{{\overset{\_}{\pi}}_{l,i} = \frac{{\overset{\_}{\overset{arrow}{p}}}_{{left} - {tpi}} - x_{i}}{w_{i}}} & (16)\end{matrix}$ $\begin{matrix}{{\overset{\_}{\pi}}_{r,i} = \frac{x_{i} + w_{i} - {\overset{\_}{\overset{arrow}{p}}}_{{right} - {tpl}}}{w_{i}}} & (17)\end{matrix}$

This may also include the processor 102 performing the bounding boxrefinement function 404 to modify the current selected bounding boxbased on the updated boundary ratio. For instance, the processor 102 canrefine the selected boundary box so that it has the updated boundaryratios within the image.

A determination is made whether one or more additional bounding boxesneed to be processed at step 526. If so, the process can return to step508 to select and refine another bounding box. Otherwise, the processcan end, and the refined bounding box(es) can be used in any suitablemanner.

Example results obtained using the process are shown in FIG. 6 , wherethe bounding box 302 is refined to a bounding box 302′ and the boundingbox 304 is refined to a bounding box 304′. The bounding box 302′ heremore accurately defines the boundary of the rear portion of one vehicle,and the bounding box 304′ here more accurately defines the boundary ofthe rear portion of another vehicle. Essentially, the bounding box 302′identifies a generally symmetrical portion of the left vehicle, and thebounding box 304′ identifies a generally symmetrical portion of theright vehicle. The ability to accurately identify the rear portions ofnearby vehicles may be used in various ways, such as to identify adirection of travel or a change in the direction of travel of the leftor right vehicle or to identify a surface of another vehicle to be usedfor depth estimation.

Although FIG. 5 illustrates one example of a method 500 forsymmetry-based boundary refinement, various changes may be made to FIG.5 . For example, while shown as a series of steps, various steps in FIG.5 may overlap, occur in parallel, occur in a different order, or occurany number of times. Although FIG. 6 illustrates one example of asymmetry-based boundary refinement, various changes may be made to FIG.6 . For instance, the contents of the image 600 and the results of thesymmetry-based boundary refinement can vary depending on the actualenvironment around the system 100.

The refined bounding boxes or other boundaries determined for an imageas described above may be used in any suitable manner. For example, insome cases, the refined bounding boxes determined for an image may beused to perform component detection as described below. Note, however,that this use of the refined bounding boxes is for illustration only.

FIG. 7 illustrates an example system 700 supporting component detectionaccording to this disclosure. The system 700 here includes many of thesame components described above with respect to the systems 100 and 400,and common reference numbers are used in the systems. As shown in FIG. 7, the processor 102 of FIG. 7 performs the object detection function 108and the bounding box refinement function 404. Note that the line segmentdetection function 110, incremental aggregation function 112, vanishingpoint detection function 114, and multi-template matching function 402are omitted from FIG. 7 for clarity but can be performed by theprocessor 102 of the system 700 in FIG. 7 .

The processor 102 in FIG. 7 is configured to perform component detectionin which one or more individual components of other vehicles (such aslicense plates or taillights) are identified. Here, the processor 102performs an integral image generation function 702, which generallyoperates to produce an integral image using the image from a camera 104a. In some embodiments, an integral image I(⋅,⋅) can be generated asfollows:

I(x,y)=Σ_(x′≤x,y′≤y) i(x′,y′)  (18)

I(x,y)=i(x,y)+I(x−1,y)+I(x,y−1)−I(x−1,y−1)  (19)

where:

I(x′,y′)=0,x′,y′<0  (20)

The integral image from the integral image generation function 702 andone or more refined bounding boxes from the bounding box refinementfunction 404 are provided to a fast block comparison function 704. Thefast block comparison function 704 generally operates to selectdifferent (mirrored) regions within each refined bounding box andanalyze the regions to compute probabilistic similarities between theregions, which can be based on the integral image. As described in moredetail below, the fast block comparison function 704 can analyzedifferent mirrored regions of a refined bounding box and look forregions that are probabilistically similar. The similarities between theregions can be used by a component detection function 706 to identifyregions in the refined bounding box associated with one or more specificcomponents of a vehicle. Example operations that can be performed by thefast block comparison function 704 and the component detection function706 are described below. One or more identified vehicle components maybe used in any suitable manner.

Note that the functions 702-706 shown in FIG. 7 and described above maybe implemented in any suitable manner in the system 700. For example, insome embodiments, various functions 702-706 may be implemented orsupported using one or more software applications or other softwareinstructions that are executed by at least one processor 102. In otherembodiments, at least some of the functions 702-706 can be implementedor supported using dedicated hardware components. In general, thefunctions 702-706 described above may be performed using any suitablehardware or any suitable combination of hardware and software/firmwareinstructions.

Although FIG. 7 illustrates one example of a system 700 supportingcomponent detection, various changes may be made to FIG. 7 . Forexample, various functions and components shown in FIG. 7 may becombined, further subdivided, replicated, omitted, or rearranged andadditional functions and components may be added according to particularneeds. Also, as noted above, the functionality for component detectionmay be used in any other suitable system, and the system may or may notrelate to automotive vehicles or other vehicles.

FIG. 8 illustrates an example method 800 for component detectionaccording to this disclosure. The method 800 may, for example, beperformed using the components of the system 700 shown in FIG. 7 . Note,however, that the method 800 may be performed using any other suitabledevice or system. Also, during the discussion of the method 800,reference is made to FIG. 9 , which illustrates an example componentdetection according to this disclosure. The example of the detectionshown in FIG. 9 is for illustration only and is merely meant toillustrate how various steps in FIG. 8 may be performed.

As shown in FIG. 8 , one or more refined bounding boxes for one or moreobjects in an image are identified at step 802. This may include, forexample, the processor 102 performing the process shown in FIG. 2 toidentify a vanishing point and performing the process shown in FIG. 5 togenerate the refined bounding box(es). An integral image of a scene isgenerated at step 804. This may include, for example, processor 102generating the integral image as described above.

A refined bounding box is selected at step 806. This may include, forexample, the processor 102 performing the fast block comparison function704 to select one of the refined bounding boxes generated by thebounding box refinement function 404. Mirrored regions in the selectedrefined bounding box are selected at step 808. This may include, forexample, the processor 102 performing the fast block comparison function704 to select mirrored regions in the selected refined bounding boxrandomly, in a predefined manner, or in any other suitable manner. Insome embodiments, the processor 102 may select a rectangular-shapedregion {right arrow over (θ)}_(left,k)=(x_(k), y_(k), w_(k), h_(k))inside the selected refined boundary box and another rectangular region{right arrow over (θ)}_(right,k) inside the selected refined boundarybox (but mirrored against a middle line of the refined boundary box).

A probabilistic distribution of each region is modeled at step 810. Thismay include, for example, the processor 102 performing the fast blockcomparison function 704 to model the probabilistic distribution of eachrectangular region or other defined region. For simplicity, a normaldistribution parameterization

_(k)˜(μ_(k), σ_(k)) may be selected, where a mean μ_(k) and a standarddeviation σ_(k) are computed using the integral image I andI′(⋅,⋅)=Σ_(x′≤x,y′≤y)i(x′, y′)². This can be expressed as:

$\begin{matrix}{{\mu_{k} = \frac{I( {\overset{arrow}{\theta}._{,k}} )}{N_{k}}},{\sigma_{k} = \sqrt{\frac{{I^{\prime}( {\overset{arrow}{\theta}._{,k}} )} - \frac{{I( {\overset{arrow}{\theta}._{,k}} )}^{2}}{N_{k}}}{N_{k}}}}} & (21)\end{matrix}$

Here, N_(k) represents a number of pixels in a region I({right arrowover (θ)}_(⋅,k)).

A probabilistic similarity between the two regions is determined at step812. This may include, for example, the processor 102 performing thefast block comparison function 704 to calculate the probabilisticsimilarity between the distributions of the two regions calculated instep 810. In some embodiments, the probabilistic similarity between tworectangular regions can be determined via their normal distributionforms

_(left),

_(right). In particular embodiments, a variant of the Kullback-Leiblerdivergence (KLD), referred to as the Jenson Shannon divergence (JSD),may be used to overcome the asymmetry associated with KLD. In theseembodiments, the probabilistic similarity may be determined as follows:

$\begin{matrix}{D_{{JS},k} = \frac{ {{{ {{{D_{{KL},k}( \mathcal{N}_{left} }}\mathcal{N}_{right}} ) + {D_{{KL},k}( \mathcal{N}_{right} }}}\mathcal{N}_{left}} )}{2}} & (22)\end{matrix}$ where: $\begin{matrix}{ {{D_{{KL},k}(}} ) = {{\log\frac{\sigma_{right}}{\sigma_{left}}} + \frac{\sigma_{left}^{2} + ( {\mu_{left} - \mu_{right}^{2}} )}{2*\sigma_{right}^{2}} - \frac{1}{2}}} & (23)\end{matrix}$

Note, however, that other measures of probabilistic similarity may beused here. The probabilistic similarity and the coordinates of the tworegions can be stored at step 814. This may include, for example, theprocessor 102 storing a tuple of {JSD value D_(JS,k), left rectangularregion {right arrow over (θ)}_(left,k), right rectangular region {rightarrow over (θ)}_(right,k)}. This may be expressed as follows:

S _(p) ←{D _(JS,k),{right arrow over (θ)}_(left,k),{right arrow over(θ)}_(right,k)}  (24)

A determination is made whether to repeat another iteration at step 816.This may include, for example, the processor 102 determining whether auser-specified number or other number of estimated similarities andregions have been identified. If not, the process can return to step808. Otherwise, finalized mirrored regions associated with the selectedrefined bounding box are identified at step 818. This may include, forexample, the processor 102 performing the component detection function706 to determine the finalized mirrored regions using the stored regioncoordinates and probabilistic similarities. In some embodiments, thecomponent detection function 706 determines the finalized mirroredregions as a weighted combination of the stored region coordinates,where the probabilistic similarities are used as the weights for thecombination. This can be expressed as:

$\begin{matrix}{{{\overset{\_}{\overset{arrow}{\theta}}}_{{left},k} = {\sum_{{k = 1},\ldots,K}{{\overset{arrow}{\theta}}_{{left},k} \cdot {\overset{\sim}{D}}_{{JS},k}}}},{{\overset{\sim}{D}}_{{JS},k} = \frac{D_{{JS},k}}{\sum_{{j = 1},\ldots,K}D_{{JS},j}}}} & (25)\end{matrix}$ $\begin{matrix}{{{\overset{\_}{\overset{arrow}{\theta}}}_{{right},k} = {\sum_{{k = 1},\ldots,K}{{\overset{arrow}{\theta}}_{{right},k} \cdot {\overset{\sim}{D}}_{{JS},k}}}},{{\overset{\sim}{D}}_{{JS},k} = \frac{D_{{JS},k}}{\sum_{{j = 1},\ldots,K}D_{{JS},j}}}} & (26)\end{matrix}$

A determination is made whether one or more additional bounding boxesneed to be processed at step 820. If so, the process can return to step806 to select and refine another bounding box. Otherwise, the processcan end, and the identified vehicle component(s) can be used in anysuitable manner.

An example of this is shown in FIG. 9 , where the image 300 is processedto identify two regions 902 and 904 within the refined bounding box304′. As can be seen here, the processor 102 has identified the tworegions 902 and 904 of the image 300 associated with the taillights ofanother vehicle since, based on the process described above, these tworegions 902 and 904 in the refined bounding box 304′ areprobabilistically similar. The same type of process may be used, forexample, to identify two regions containing the license plate of theother vehicle. The positions of these components of the other vehiclemay be used for various purposes, such as identifying a direction oftravel or a change in the direction of travel of the other vehiclerelative to the target vehicle or identifying a surface of the othervehicle to be used for depth estimation.

Although FIG. 8 illustrates one example of a method 800 for componentdetection, various changes may be made to FIG. 8 . For example, whileshown as a series of steps, various steps in FIG. 8 may overlap, occurin parallel, occur in a different order, or occur any number of times.Although FIG. 9 illustrates one example of a component detection,various changes may be made to FIG. 9 . For instance, the contents ofthe image 900 and the results of the component detection can varydepending on the actual environment around the system 100.

FIG. 10 illustrates an example usage of vanishing point determination,symmetry-based boundary refinement, and/or component detection accordingto this disclosure. A system 1000 here includes many of the samecomponents described above with respect to the systems 1000, 400, and700, and common reference numbers are used in the systems. As shown inFIG. 10 , the processor 102 of FIG. 10 performs the vanishing pointdetection function 114, the bounding box refinement function 404, andthe component detection function 706. Note that the other functionsdescribed above are omitted from FIG. 10 for clarity but can beperformed by the processor 102 of the system 1000 in FIG. 10 .

The processor 102 performs a decision planning function 1002, whichgenerally uses one or more determined vanishing points, one or morerefined bounding boxes, and/or one or more identified vehicle componentsto determine how to adjust the operation of the system 1000. Forexample, in an automotive vehicle, the decision planning function 1002may determine whether (and how) to change the steering direction of thevehicle, whether (and how) to apply the brakes or accelerate thevehicle, or whether (and how) to trigger an audible, visible, haptic, orother warning. The warning may indicate that the system 1000 is nearanother vehicle, obstacle, or person, is departing from a current lanein which the vehicle is traveling, or is approaching a possible impactlocation with another vehicle, obstacle, or person. In general, theidentified adjustments determined by the decision planning function 1002can vary widely based on the specific application.

The decision planning function 1002 can interact with one or morecontrol functions 1004, each of which can be used to adjust or controlthe operation of one or more actuators 1006 in the system 1000. Forexample, in an automotive vehicle, the one or more actuators 1006 mayrepresent one or more brakes, electric motors, or steering components ofthe vehicle, and the control function(s) 1004 can be used to apply ordiscontinue application of the brakes, speed up or slow down theelectric motors, or change the steering direction of the vehicle. Ingeneral, the specific way(s) in which detected objects can be used mayvary depending on the specific system 1000 in which object detection isbeing used.

Note that the functions 1002-1006 shown in FIG. 10 and described abovemay be implemented in any suitable manner in the system 100. Forexample, in some embodiments, various functions 1002-1006 may beimplemented or supported using one or more software applications orother software instructions that are executed by at least one processor102. In other embodiments, at least some of the functions 1002-1006 canbe implemented or supported using dedicated hardware components. Ingeneral, the functions 1002-1006 described above may be performed usingany suitable hardware or any suitable combination of hardware andsoftware/firmware instructions.

Although FIG. 10 illustrates one example usage of vanishing pointdetermination, symmetry-based boundary refinement, and/or componentdetection, various changes may be made to FIG. 10 . For example, variousfunctions and components shown in FIG. 10 may be combined, furthersubdivided, replicated, omitted, or rearranged and additional functionsand components may be added according to particular needs. Also, whilevanishing point determination, symmetry-based boundary refinement, andcomponent detection are used here, the system 1000 may use a single oneof these functions or any desired combination of these functions.

Note that many functional aspects of the embodiments described above canbe implemented using any suitable hardware or any suitable combinationof hardware and software/firmware instructions. In some embodiments, atleast some functional aspects of the embodiments described above can beembodied as software instructions that are executed by one or moreunitary or multi-core central processing units or other processingdevice(s). In other embodiments, at least some functional aspects of theembodiments described above can be embodied using one or moreapplication specific integrated circuits (ASICs). When implemented usingone or more ASICs, any suitable integrated circuit design andmanufacturing techniques may be used, such as those that can beautomated using electronic design automation (EDA) tools. Examples ofsuch tools include tools provided by SYNOPSYS, INC., CADENCE DESIGNSYSTEMS, INC., and SIEMENS EDA.

FIG. 11 illustrates an example design flow 1100 for employing one ormore tools to design hardware that implements one or more controlfunctions according to this disclosure. More specifically, the designflow 1100 here represents a simplified ASIC design flow employing one ormore EDA tools or other tools for designing and facilitating fabricationof ASICs that implement at least some functional aspects of the variousembodiments described above.

As shown in FIG. 11 , a functional design of an ASIC is created at step1102. For any portion of the ASIC design that is digital in nature, insome cases, this may include expressing the digital functional design bygenerating register transfer level (RTL) code in a hardware descriptivelanguage (HDL), such as VHDL or VERILOG. A functional verification (suchas a behavioral simulation) can be performed on HDL data structures toensure that the RTL code that has been generated is in accordance withlogic specifications. In other cases, a schematic of digital logic canbe captured and used, such as through the use of a schematic captureprogram. For any portion of the ASIC design that is analog in nature,this may include expressing the analog functional design by generating aschematic, such as through the use of a schematic capture program. Theoutput of the schematic capture program can be converted (synthesized),such as into gate/transistor level netlist data structures. Datastructures or other aspects of the functional design are simulated, suchas by using a simulation program with integrated circuits emphasis(SPICE), at step 1104. This may include, for example, using the SPICEsimulations or other simulations to verify that the functional design ofthe ASIC performs as expected.

A physical design of the ASIC is created based on the validated datastructures and other aspects of the functional design at step 1106. Thismay include, for example, instantiating the validated data structureswith their geometric representations. In some embodiments, creating aphysical layout includes “floor-planning,” where gross regions of anintegrated circuit chip are assigned and input/output (I/O) pins aredefined. Also, hard cores (such as arrays, analog blocks, inductors,etc.) can be placed within the gross regions based on design constraints(such as trace lengths, timing, etc.). Clock wiring, which is commonlyreferred to or implemented as clock trees, can be placed within theintegrated circuit chip, and connections between gates/analog blocks canbe routed within the integrated circuit chip. When all elements havebeen placed, a global and detailed routing can be performed to connectall of the elements together. Post-wiring optimization may be performedto improve performance (such as timing closure), noise (such as signalintegrity), and yield. The physical layout can also be modified wherepossible while maintaining compliance with design rules that are set bya captive, external, or other semiconductor manufacturing foundry ofchoice, which can make the ASIC more efficient to produce in bulk.Example modifications may include adding extra vias or dummymetal/diffusion/poly layers.

The physical design is verified at step 1108. This may include, forexample, performing design rule checking (DRC) to determine whether thephysical layout of the ASIC satisfies a series of recommendedparameters, such as design rules of the foundry. In some cases, thedesign rules represent a series of parameters provided by the foundrythat are specific to a particular semiconductor manufacturing process.As particular examples, the design rules may specify certain geometricand connectivity restrictions to ensure sufficient margins to accountfor variability in semiconductor manufacturing processes or to ensurethat the ASICs work correctly. Also, in some cases, a layout versusschematic (LVS) check can be performed to verify that the physicallayout corresponds to the original schematic or circuit diagram of thedesign. In addition, a complete simulation may be performed to ensurethat the physical layout phase is properly done.

After the physical layout is verified, mask generation design data isgenerated at step 1110. This may include, for example, generating maskgeneration design data for use in creating photomasks to be used duringASIC fabrication. The mask generation design data may have any suitableform, such as GDSII data structures. This step may be said to representa “tape-out” for preparation of the photomasks. The GDSII datastructures or other mask generation design data can be transferredthrough a communications medium (such as via a storage device or over anetwork) from a circuit designer or other party to a photomasksupplier/maker or to the semiconductor foundry itself. The photomaskscan be created and used to fabricate ASIC devices at step 1112.

Although FIG. 11 illustrates one example of a design flow 1100 foremploying one or more tools to design hardware that implements one ormore vehicle control functions, various changes may be made to FIG. 11 .For example, at least some functional aspects of the various embodimentsdescribed above may be implemented in any other suitable manner.

FIG. 12 illustrates an example device 1200 supporting execution of oneor more tools to design hardware that implements one or more vehiclecontrol functions according to this disclosure. The device 1200 may, forexample, be used to implement at least part of the design flow 1100shown in FIG. 11 . However, the design flow 1100 may be implemented inany other suitable manner.

As shown in FIG. 12 , the device 1200 denotes a computing device orsystem that includes at least one processing device 1202, at least onestorage device 1204, at least one communications unit 1206, and at leastone input/output (I/O) unit 1208. The processing device 1202 may executeinstructions that can be loaded into a memory 1210. The processingdevice 1202 includes any suitable number(s) and type(s) of processors orother processing devices in any suitable arrangement. Example types ofprocessing devices 1202 include one or more microprocessors,microcontrollers, digital signal processors (DSPs), application specificintegrated circuits (ASICs), field programmable gate arrays (FPGAs), ordiscrete circuitry.

The memory 1210 and a persistent storage 1212 are examples of storagedevices 1204, which represent any structure(s) capable of storing andfacilitating retrieval of information (such as data, program code,and/or other suitable information on a temporary or permanent basis).The memory 1210 may represent a random access memory or any othersuitable volatile or non-volatile storage device(s). The persistentstorage 1212 may contain one or more components or devices supportinglonger-term storage of data, such as a read only memory, hard drive,Flash memory, or optical disc.

The communications unit 1206 supports communications with other systemsor devices. For example, the communications unit 1206 can include anetwork interface card or a wireless transceiver facilitatingcommunications over a wired or wireless network. The communications unit1206 may support communications through any suitable physical orwireless communication link(s).

The I/O unit 1208 allows for input and output of data. For example, theI/O unit 1208 may provide a connection for user input through akeyboard, mouse, keypad, touchscreen, or other suitable input device.The I/O unit 1208 may also send output to a display or other suitableoutput device. Note, however, that the I/O unit 1208 may be omitted ifthe device 1200 does not require local I/O, such as when the device 1200represents a server or other device that can be accessed remotely.

The instructions that are executed by the processing device 1202 includeinstructions that implement at least part of the design flow 1100. Forexample, the instructions that are executed by the processing device1202 may cause the processing device 1202 to generate or otherwiseobtain functional designs, perform simulations, generate physicaldesigns, verify physical designs, perform tape-outs, or create/usephotomasks (or any combination of these functions). As a result, theinstructions that are executed by the processing device 1202 support thedesign and fabrication of ASIC devices or other devices that implementone or more vehicle control functions described above.

Although FIG. 12 illustrates one example of a device 1200 supportingexecution of one or more tools to design hardware that implements one ormore vehicle control functions, various changes may be made to FIG. 12 .For example, computing and communication devices and systems come in awide variety of configurations, and FIG. 12 does not limit thisdisclosure to any particular computing or communication device orsystem.

Note that while the various functions described above are oftendescribed as using or being based on images captured using one or morecameras 104 a, other types of sensors 104 may be used to provide sensordata for use by these functions. For example, abstracted imagerepresentations or pseudo-image representations of RADAR or LIDARmeasurements may be processed as described above to identify vanishingpoints, perform symmetry-based boundary refinement, and/or engage incomponent detection.

Also note that while bounding boxes are often described above as beinggenerated, refined, processed, or otherwise used to represent theboundaries of objects, these boundaries may be expressed in any othersuitable manner. Example types of boundaries that may be defined andused in the various systems, techniques, and processes described aboveinclude two-dimensional bounding boxes, three-dimensional cuboids,and/or boundaries around recognized objects (such as bridges, buildings,trees, light poles or other poles, traffic signs, traffic lights, etc.).

In some embodiments, various functions described in this patent documentare implemented or supported using machine-readable instructions thatare stored on a non-transitory machine-readable medium. The phrase“machine-readable instructions” includes any type of instructions,including source code, object code, and executable code. The phrase“non-transitory machine-readable medium” includes any type of mediumcapable of being accessed by one or more processing devices or otherdevices, such as a read only memory (ROM), a random access memory (RAM),a Flash memory, a hard disk drive (HDD), or any other type of memory. A“non-transitory” medium excludes wired, wireless, optical, or othercommunication links that transport transitory electrical or othersignals. Non-transitory media include media where data can bepermanently stored and media where data can be stored and lateroverwritten.

It may be advantageous to set forth definitions of certain words andphrases used throughout this patent document. The terms “include” and“comprise,” as well as derivatives thereof, mean inclusion withoutlimitation. The term “or” is inclusive, meaning and/or. The phrase“associated with,” as well as derivatives thereof, may mean to include,be included within, interconnect with, contain, be contained within,connect to or with, couple to or with, be communicable with, cooperatewith, interleave, juxtapose, be proximate to, be bound to or with, have,have a property of, have a relationship to or with, or the like. Thephrase “at least one of,” when used with a list of items, means thatdifferent combinations of one or more of the listed items may be used,and only one item in the list may be needed. For example, “at least oneof: A, B, and C” includes any of the following combinations: A, B, C, Aand B, A and C, B and C, and A and B and C.

The description in the present application should not be read asimplying that any particular element, step, or function is an essentialor critical element that must be included in the claim scope. The scopeof patented subject matter is defined only by the allowed claims.Moreover, none of the claims invokes 35 U.S.C. § 112(f) with respect toany of the appended claims or claim elements unless the exact words“means for” or “step for” are explicitly used in the particular claim,followed by a participle phrase identifying a function. Use of termssuch as (but not limited to) “mechanism,” “module,” “device,” “unit,”“component,” “element,” “member,” “apparatus,” “machine,” “system,”“processor,” or “controller” within a claim is understood and intendedto refer to structures known to those skilled in the relevant art, asfurther modified or enhanced by the features of the claims themselves,and is not intended to invoke 35 U.S.C. § 112(f).

While this disclosure has described certain embodiments and generallyassociated methods, alterations and permutations of these embodimentsand methods will be apparent to those skilled in the art. Accordingly,the above description of example embodiments does not define orconstrain this disclosure. Other changes, substitutions, and alterationsare also possible without departing from the spirit and scope of thisdisclosure, as defined by the following claims.

What is claimed is:
 1. A method comprising: obtaining, using at leastone processing device, a refined boundary identifying a specifiedportion of a detected object within a scene, the refined boundaryassociated with image data; repeatedly, during multiple iterations andusing the at least one processing device, (i) identifying multipleregions within the refined boundary and (ii) determining a similarity ofthe image data contained within the multiple regions; and identifying,using the at least one processing device, one or more locations of oneor more components of the detected object based on the identifiedregions and the determined similarities.
 2. The method of claim 1,further comprising: obtaining an image of the scene, the imagecomprising the image data; generating an integral image of the scene;and during each iteration, determining a probabilistic distributionassociated with each of the multiple regions based on the integralimage.
 3. The method of claim 2, wherein the probabilistic distributionsrepresent normal distribution parameterizations.
 4. The method of claim2, wherein, during each iteration, determining the similarity of theimage data contained within the multiple regions comprises determining adivergence between the probabilistic distributions associated with themultiple regions.
 5. The method of claim 1, wherein: each iterationgenerates coordinates based on the identified regions within the refinedboundary; and identifying the one or more locations of the one or morecomponents of the detected object comprises generating a weightedcombination of the coordinates while using the determined similaritiesas weights for the coordinates, the one or more locations of the one ormore components of the detected object representing the weightedcombination.
 6. The method of claim 1, wherein, during each iteration,the multiple regions within the refined boundary include a first regionand a mirrored second region within the refined boundary.
 7. The methodof claim 1, further comprising: determining a position of a vanishingpoint based on multiple collections of line segments in an imagecomprising the image data; and identifying the refined boundary based onan original boundary, the original boundary refined based on thevanishing point.
 8. The method of claim 1, wherein: the detected objectcomprises a vehicle; the specified portion of the detected objectcomprises a rear portion of the vehicle; and the one or more componentsof the vehicle comprise at least one of: one or more taillights of thevehicle or a license plate of the vehicle.
 9. The method of claim 1,further comprising: identifying at least one action to be performedbased on the one or more locations of the one or more components of thedetected object; and performing the at least one action.
 10. The methodof claim 9, wherein the at least one action comprises at least one of:an adjustment to at least one of: a steering of a vehicle, a speed ofthe vehicle, an acceleration of the vehicle, and a braking of thevehicle; and an activation of an audible, visible, or haptic warning.11. An apparatus comprising: at least one processing device configuredto: obtain a refined boundary identifying a specified portion of adetected object within a scene, the refined boundary associated withimage data; repeatedly, during multiple iterations, (i) identifymultiple regions within the refined boundary and (ii) determine asimilarity of the image data contained within the multiple regions; andidentify one or more locations of one or more components of the detectedobject based on the identified regions and the determined similarities.12. The apparatus of claim 11, wherein the at least one processingdevice is further configured to: obtain an image of the scene, the imagecomprising the image data; generate an integral image of the scene; andduring each iteration, determine a probabilistic distribution associatedwith each of the multiple regions based on the integral image.
 13. Theapparatus of claim 12, wherein the probabilistic distributions representnormal distribution parameterizations.
 14. The apparatus of claim 12,wherein, to determine the similarity of the image data contained withinthe multiple regions during each iteration, the at least one processingdevice is configured to determine a divergence between the probabilisticdistributions associated with the multiple regions.
 15. The apparatus ofclaim 11, wherein: during each iteration, the at least one processingdevice is configured to generate coordinates based on the identifiedregions within the refined boundary; and to identify the one or morelocations of the one or more components of the detected object, the atleast one processing device is configured to generate a weightedcombination of the coordinates while using the determined similaritiesas weights for the coordinates, the one or more locations of the one ormore components of the detected object representing the weightedcombination.
 16. The apparatus of claim 11, wherein, during eachiteration, the multiple regions within the refined boundary include afirst region and a mirrored second region within the refined boundary.17. The apparatus of claim 11, wherein the at least one processingdevice is further configured to: determine a position of a vanishingpoint based on multiple collections of line segments in an imagecomprising the image data; and identify the refined boundary based on anoriginal boundary, the original boundary refined based on the vanishingpoint.
 18. The apparatus of claim 11, wherein: the detected objectcomprises a vehicle; the specified portion of the detected objectcomprises a rear portion of the vehicle; and the one or more componentsof the vehicle comprise at least one of: one or more taillights of thevehicle or a license plate of the vehicle.
 19. The apparatus of claim11, wherein the at least one processing device is further configured to:identify at least one action to be performed based on the one or morelocations of the one or more components of the detected object; andperform the at least one action.
 20. The apparatus of claim 19, whereinthe at least one action comprises at least one of: an adjustment to atleast one of: a steering of a vehicle, a speed of the vehicle, anacceleration of the vehicle, and a braking of the vehicle; and anactivation of an audible, visible, or haptic warning.
 21. Anon-transitory machine-readable medium containing instructions that whenexecuted cause at least one processor to: obtain a refined boundaryidentifying a specified portion of a detected object within a scene, therefined boundary associated with image data; repeatedly, during multipleiterations, (i) identify multiple regions within the refined boundaryand (ii) determine a similarity of the image data contained within themultiple regions; and identify one or more locations of one or morecomponents of the detected object based on the identified regions andthe determined similarities.
 22. The non-transitory machine-readablemedium of claim 21, further containing instructions that when executedcause the at least one processor to: obtain an image of the scene, theimage comprising the image data; generate an integral image of thescene; and during each iteration, determine a probabilistic distributionassociated with each of the multiple regions based on the integralimage.
 23. The non-transitory machine-readable medium of claim 22,wherein the probabilistic distributions represent normal distributionparameterizations.
 24. The non-transitory machine-readable medium ofclaim 22, wherein the instructions that when executed cause the at leastone processor to determine the similarity of the image data containedwithin the multiple regions during each iteration comprise instructionsthat when executed cause the at least one processor to determine adivergence between the probabilistic distributions associated with themultiple regions.
 25. The non-transitory machine-readable medium ofclaim 21, wherein: the instructions when executed cause the at least oneprocessor to generate, during each iteration, coordinates based on theidentified regions within the refined boundary; and the instructionsthat when executed cause the at least one processor to identify the oneor more components of the detected object comprise instructions thatwhen executed cause the at least one processor to generate a weightedcombination of the coordinates while using the determined similaritiesas weights for the coordinates, the one or more locations of the one ormore components of the detected object representing the weightedcombination.
 26. The non-transitory machine-readable medium of claim 21,wherein, during each iteration, the multiple regions within the refinedboundary include a first region and a mirrored second region within therefined boundary.
 27. The non-transitory machine-readable medium ofclaim 21, further containing instructions that when executed cause theat least one processor to: determine a position of a vanishing pointbased on multiple collections of line segments in an image comprisingthe image data; and identify the refined boundary based on an originalboundary, the original boundary refined based on the vanishing point.28. The non-transitory machine-readable medium of claim 21, wherein: thedetected object comprises a vehicle; the specified portion of thedetected object comprises a rear portion of the vehicle; and the one ormore components of the vehicle comprise at least one of: one or moretaillights of the vehicle or a license plate of the vehicle.
 29. Thenon-transitory machine-readable medium of claim 21, further containinginstructions that when executed cause the at least one processor to:identify at least one action to be performed based on the one or morelocations of the one or more components of the detected object; andperform the at least one action.
 30. The non-transitory machine-readablemedium of claim 29, wherein the at least one action comprises at leastone of: an adjustment to at least one of: a steering of a vehicle, aspeed of the vehicle, an acceleration of the vehicle, and a braking ofthe vehicle; and an activation of an audible, visible, or hapticwarning.